The field of hydrocarbon production is directed to retrieving hydrocarbons that are trapped in subsurface reservoirs. These hydrocarbons can be recovered by drilling wells into the reservoirs such that hydrocarbons are able to flow from the reservoirs into the wells and up to the surface. The geology of a reservoir has a large impact on the production rate at which hydrocarbons are able to flow into a well. A large amount of effort has therefore, been dedicated to developing reservoir characterization and simulation techniques to better predict how fluid will flow within a reservoir.
Highly complex geological subsurface reservoirs, such as fractured reservoirs, present unique and specialized challenges with regards to forecasting fluid flow. A fractured reservoir is a reservoir in which a network of fractures enhances the permeability field, thereby significantly affecting well productivity and recovery efficiency. Fractures can be described as open cracks or voids embedded within the rock matrix, and can either be naturally occurring or artificially generated from a wellbore. Natural fractures typically occur in sets of parallel fractures that can range several orders of magnitude in size. The length distribution within a fracture set is characteristically non-linear, with many short fractures and a diminishing number of large fractures. The range of fracture apertures, which can be considered as the width of the fracture, is distributed in a similar manner. Furthermore, several fracture sets can coexist in a rock forming connected networks of significant extent and complexity.
FIG. 1 shows a schematic illustrating a physical geologic volume of a fractured reservoir 10 having a plurality of strata 11. The plurality of strata 11 are typically composed of parallel layers of rock and fluid material each characterized by different sedimentological and fluid properties. Fractures or fracture networks 13 are embedded within the rock matrix 15 and can play an important role in allowing fluids to flow through the reservoir to reach a well. For example, fracture network 13 often produces fluid to a intersecting production well at a rate that greatly exceeds the rate of flow from the rock matrix 15 to the well, as the fracture network 13 typically has a much greater capability to transport fluids. Accordingly, a network of multiple intersecting fractures often forms the basis for flow in fractured reservoirs.
Fracture representations, which are often utilized in reservoir modeling to represent a network of fractures within a subsurface reservoir, can be generated to accurately predict reservoir fluid flow characteristics. One method of generating a fracture representation includes progressing from observations of discrete fractures that intersect wellbores to a field-wide bulk distribution of fractures. Borehole image (BHI) logs make detailed images of the walls of a wellbore using resistivity or ultrasonic acoustic measurements. These images are used to determine the location, orientation, and aperture (width) of fractures intersected by the wellbore. Thus, a BHI log may show the total population of fractures that intersect a well. Additional data describing fractures intersecting a particular well can be obtained from sources such as cores, drilling information, production logs, and down-hole well measurements such as a temperature survey. This empirical data can be used to judge whether a particular fracture possesses the size and connectivity to affect fluid flow to a wellbore by a sufficient amount. The data associated with fractures that sufficiently affect fluid flow can be used to assemble logs of fracture density, which can be described as the fracture surface area per unit volume.
Artificial neural network techniques can be employed to generate a spatial distribution of fracture density. Such techniques are well known by those skilled in the art and allow for distribution of the fracture density using regression analysis based on the data describing the fractures. The fracture density can be resealed to match the observed distribution of fracture density at the wells and throughout the reservoir by using additional geostatistical techniques. For instance, neural network results can be re-scaled using Sequential Gaussian Simulation (SGS) collocated co-kriging, a technique well known in the art, wherein the neural network fracture density distribution is used as highly-correlated soft data. The resealed fracture density distribution can then be used to stochastically generate a fracture representation describing the network of fractures within the reservoir. In particular, the resealed fracture density distribution can be used as a constraint during the stochastic generation of the fracture representation. Fracture dimensions and orientation data can also be used to constrain the fracture representation.
There are many commercially available products for constructing fracture representations in this manner, such as FracMan™ distributed by Golder Associates Inc. headquartered in Atlanta, Ga. The stochastic fracture representation can be imported into a geological reservoir model to construct a more realistic geological characterization of a fractured subsurface reservoir. These realistic geological reservoir models are typically in the form of highly-resolved discrete fracture models (DFMs) that explicitly define each fracture.
FIG. 2 illustrates a fractured subsurface reservoir domain 20 having fracture representation 21. In this example, domain 20 contains forty (40) discrete fractures 23 that are represented by 2D polygons. While discrete fractures 23 within fracture representation 21 are represented as planar rectangles extending within domain 20, one skilled in the art will appreciate that discrete fractures 23 could be represented by other 2D or 3D geometric shapes. Discrete fractures 23 can extend within domain 20 such that they do not penetrate other discrete fractures 23, such as discrete fracture 25. However, as previously discussed, discrete fractures 23 often intersect one another forming connected networks of fractures of significant extent and complexity, such as fracture network 27.
While fracture representations define each fracture, the fractures intersecting the well are typically only constrained by its appearance in a conventional core, image log, or other borehole data. Therefore, while these fracture representations can estimate some of the fracture network parameters, the effective size (length and height) of a fracture away from the well remains a major unknown. This can contribute to considerable well-to-well production variability and makes performance predictions highly uncertain for fractured reservoirs.